| CO2 Emission Variables | Minimum | Mean | Maximum | |
|---|---|---|---|---|
| Total CO2 (thousand metric tons) | Total CO2 (thousand metric tons) | -1473.00 | 22687.119661 | 2806634.00 |
| Solid Fuel Consumption | Solid Fuel Consumption | -103.00 | 11202.723867 | 2045156.00 |
| Liquid Fuel Consumption | Liquid Fuel Consumption | -4663.00 | 7680.005109 | 680284.00 |
| Gas Fuel Consumption | Gas Fuel Consumption | -40.00 | 3227.981971 | 390719.00 |
| Cement Production | Cement Production | 0.00 | 638.453865 | 338912.00 |
| Gas Flaring | Gas Flaring | 0.00 | 276.163457 | 20520.00 |
| Per Capita CO2 (metric tons) | Per Capita CO2 (metric tons) | -0.68 | 1.268883 | 45.96 |
| Bunker Fuels | Bunker Fuels | 0.00 | 560.330606 | 45630.00 |
Global CO2 Emissions
Carbon dioxide (CO2) emissions represent a paramount area of interest for scholars, policymakers, and the global community at large due to their pivotal role in climate change dynamics. As a greenhouse gas, CO2 contributes significantly to the enhancement of Earth’s natural greenhouse effect, trapping heat and leading to a rise in global temperatures. This phenomenon, commonly referred to as anthropogenic global warming, is principally driven by human activities such as the combustion of fossil fuels, deforestation, and industrial processes. The consequences of heightened CO2 concentrations are multifaceted, encompassing rising sea levels, more frequent and severe weather events, disruptions in ecosystems, and threats to human health and socio-economic stability. Consequently, understanding, monitoring, and mitigating CO2 emissions have become imperative endeavors to address the complexities of climate change and to formulate effective strategies for sustainable environmental management and global resilience.
The Data
The data under examination originates from the Global, Regional, and National Fossil-Fuel CO2 Emissions dataset compiled by Boden, Marland, and Andres in 2013 (Boden, Marland, & Andres, 2013). Accessible in the form of a ZIP archive named CSV-FILES, this dataset serves as a valuable resource for understanding carbon dioxide (CO2) emissions worldwide. The dataset encompasses a wide range of information spanning multiple years and is instrumental in analyzing emissions from various countries. The sources of emissions are categorized into solid fuels, liquid fuels, gas fuels, cement production, gas flaring, bunker fuels, while also providing data on per capita CO2 emissions and total CO2 emissions for 259 nations. This comprehensive compilation allows for a nuanced examination of global and regional trends in CO2 emissions, facilitating a detailed exploration of the contributing factors to climate change. The dataset, being rich in detail and scope, provides researchers and policymakers with a robust foundation for formulating strategies to address the complexities of carbon emissions on a global scale.
Components of Emissions
The Total Fossil Fuel Emissions for G7 vs Non-g7 Countries graph shows that G7 countries are the primary emitters of fossil fuels globally. Possible interpretations could be that G7 countries are more developed and have higher energy demands, or that G7 countries have played a larger role in the development of industries that rely heavily on fossil fuels, such as transportation and manufacturing compared to Non-G7 countries. These may be the primary reasons for the higher fossil fuel emissions from G7 countries. This highlights the importance of G7 countries taking action to reduce their emissions and help to mitigate climate change.
The dots above Non-G7 in the graph are outliers. These are data points that are significantly different from the rest of the data set. In this case, the outliers are countries with non-G7 emissions that are much higher than the average for non-G7 countries.
There are a few possible explanations for the outliers:
The outliers may be countries that are rapidly industrializing and have a high demand for energy, or countries that have a large amount of natural resources, such as coal or oil, and produce these resources for export. They could also be countries that have not implemented policies to reduce their emissions. The outliers here are still part of the non-G7 group of countries which means that the overall trend for non-G7 countries is still lower emissions than for G7 countries. However, the outliers show that there is a great deal of variation within the non-G7 group of countries.
| Source | p.value | adjusted_p.value |
|---|---|---|
| Emissions.from.Gas.Fuels | 0.0000000 | 0.0000000 |
| Emissions.from.Liquid.Fuels | 0.0349384 | 0.2096303 |
| Emissions.from.Solid.Fuels | 0.1144837 | 0.6869025 |
| Emissions.from.Gas.Flaring | 0.7323487 | 1.0000000 |
| Emissions.from.Cement.Production | 0.4477030 | 1.0000000 |
| Emissions.from.Bunker.Fuels | 0.0553599 | 0.3321593 |
The p-value for the emissions from Gas Fuels is 1.75e-14 and the adjusted p-value is 1.05e-13. The p-value is extremely low, indicating a significant difference in emissions from Gas Fuels between G7 and non-G7 countries. The adjusted p-value remains significant after correction. This means that, even after adjusting for multiple comparisons, the result is still considered statistically significant. This suggests that the observed difference in emissions from Gas Fuels between G7 and non-G7 countries is unlikely to be due to random chance.
The p-value for the emissions from Liquid Fuels is 0.035 and the adjusted p-value is 0.21. The p-value is below the conventional threshold of 0.05, suggesting a significant difference. However, after adjusting for multiple comparisons, the result is no longer significant. This suggests that any observed differences in emissions from sources like Liquid Fuels between G7 and non-G7 countries could be due to random variability.
The p-value for the emissions from Solid Fuels is 0.114 and the adjusted p-value is 0.687. The p-value is not below 0.05, indicating no significant difference in emissions from Solid Fuels between the two groups. This result holds after adjusting for multiple comparisons. The differences observed in emissions from sources like Gas Fuels, Liquid Fuels, or others between G7 and non-G7 countries are unlikely to be due to random chance alone.
The p-value for the emissions from Gas Flaring is 0.732 and the adjusted p-value is 1.0. The p-value is high, suggesting no significant difference in emissions from Gas Flaring. This result holds even after adjustment.
The p-value for the emissions from Cement Production is 0.448 and the adjusted p-value is 1.0. The p-value is not below 0.05, indicating no significant difference in emissions from Cement Production between G7 and non-G7 countries. This result holds after adjusting for multiple comparisons.
The p-value for the emissions from Bunker Fuels is 0.055 and the adjusted p-value is 0.332. The p-value is slightly above 0.05, indicating a marginally significant difference. However, after adjusting for multiple comparisons, the result is no longer significant. This suggests that any observed differences in emissions from sources like Bunker Fuels between G7 and non-G7 countries could be due to random variability.
Temporal US Trends
Highest correlation: TAIWAN REPUBLIC OF KOREA and REPUBLIC OF KOREA TAIWAN with correlation = 0.9936041 Lowest correlation: UNITED KINGDOM ECUADOR and ECUADOR UNITED KINGDOM with correlation = -0.8602864 
Pearson Correlation Coefficient (r):
The countries Taiwan and Republic of Korea have the highest Correlation Value of 0.9936041. This high positive correlation (close to 1) suggests that the total emissions between Taiwan and the Republic of Korea are strongly positively related. When one country experiences an increase or decrease in emissions, the other country tends to follow a similar pattern.
The countries United Kingdom and Ecuador have the lowest Correlation Value of -0.8602864. This high negative correlation (close to -1) indicates a strong negative relationship between the total emissions of the United Kingdom and Ecuador. When one country’s emissions increase, the other tends to decrease, and vice versa.
For the Linear Regression: United States Total CO2 (1970-2010) graph the R-squared value is 0.7765, suggesting that the model explains about 77.65% of the variance in the response variable (TotalCO2). There is a significant amount of unexplained variation. The adjusted R-squared is 0.7708, which adjusts the R-squared value for the number of predictors in the model.
The residuals in the Residuals Plot show a clear downward trend, which suggests that the model is overestimating the values for smaller fitted values and underestimating the values for larger fitted values. Overall, this is not a good model. The residuals are not reasonably behaved, and the model is not adequately explaining the variation in the data.
The Linear Regression graph depicts that total CO2 emissions appear to increase between the years 1970 and 2010 for the United States. The coefficients table shows that the intercept is significantly different from zero (p-value: 2.67e-13), and the coefficient for “Year” is also significant (p-value: 2.93e-14). This suggests that there is a statistically significant linear relationship between the year and total CO2 emissions for the United States.
Global Trends
The Bartlett test suggests a violation of the homogeneity of variances assumption. This indicates that using a traditional ANOVA (or Welch’s ANOVA) might not be appropriate, and a non-parametric test like the Kruskal-Wallis test (followed by the Dunn test for post-hoc comparisons) is a reasonable choice shown below.
Here are some key findings based on the Dunn test results:
Asia has significantly higher total fossil fuel emissions compared to Africa, Europe, North America, and South America. Australia has significantly higher total fossil fuel emissions compared to Africa, Europe, and South America. Europe has significantly lower total fossil fuel emissions compared to Asia, Australia, and North America. North America has significantly higher total fossil fuel emissions compared to Africa, Europe, and South America. South America has significantly lower total fossil fuel emissions compared to Africa, Asia, Australia, and North America.
The Kruskal-Wallis test suggests overall differences among the continents, and the pairwise comparisons provide insights into which specific pairs are significantly different in terms of total fossil fuel emissions.
The Kruskal-Wallis tests indicate that there are significant differences in total fossil fuel emissions among continents for both 1984 and 2014. The Dunn tests for pairwise comparisons provide more details about which specific continent pairs are significantly different. Here are some key observations:
In 1984, the United States was the world’s leading producer of greenhouse gases. Overall the total fossil fuel emissions in 1984 were lower on average compared to 2014.
Asia has significantly different total fossil fuel emissions compared to Africa, Europe, and North America. Australia has significantly different total fossil fuel emissions compared to Asia and Europe. Europe has significantly different total fossil fuel emissions compared to Africa, Asia, and North America. North America has significantly different total fossil fuel emissions compared to Asia and Europe.
By 2014, the order of the continents by emissions had changed. Asia had become the world’s leading emitter. China specifically is the country that leads in 2014 emitting of fossil fuels which is the top dot, or outlier, on the graph for Asia in 2014.
Asia has significantly different total fossil fuel emissions compared to Africa, Europe, and North America. Australia has significantly different total fossil fuel emissions compared to Asia, Europe, and North America. Europe has significantly different total fossil fuel emissions compared to Africa, Asia, and North America. North America has significantly different total fossil fuel emissions compared to Asia, Europe, and South America. These results suggest that the differences in total fossil fuel emissions among continents persist and may have evolved over time.
Reference
Boden, T.A., G. Marland, and R.J. Andres. 2013. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi.